基于GAN的低轨卫星互联网观测数据稳健合成框架
A GAN-Based Framework for Robust Data Synthesis in Satellite Internet Observations
AI总结：
针对低轨卫星互联网观测数据缺失问题，提出基于生成式人工智能的框架，设计数据缺失场景，用最新模型评估，结果显示该框架有效，GT - GAN模型表现最佳，为相关研究指明未来方向。
AI中文摘要：
低轨卫星互联网已成为实现普遍连接的重要基础设施，但当前观测常存在数据缺失，使数据增强任务复杂并限制数据集扩展。鉴于数据集的复杂特性，生成式人工智能是一种有前景的方法，但在该领域应用较少。本文提出基于生成式人工智能的框架，直接从不完整的低轨网络观测中合成高保真数据。提出代表性数据缺失场景，用最新基于GAN和VAE的生成式人工智能模型在WetLinks数据集上评估性能。设计块级和点级缺失场景以模拟现实中低轨卫星网络的数据丢失。结果表明基于GAN的框架有效，GT - GAN模型在两种缺失场景中表现最佳，即使在极端条件下也能稳健捕捉潜在数据分布。这些结果为基于生成式人工智能的数据增强方法及卫星网络测量的数据驱动研究指明了未来方向。
英文摘要：
Low-Earth orbit (LEO) satellite Internet has become an important infrastructure for enabling ubiquitous connectivity to align with the International Telecommunications Union vision for 6G telecommunications networks. However, current LEO satellite Internet observations often suffer from missing data, which complicates data augmentation task and limits the expansion of representative datasets. Given the complex characteristics of these datasets, generative AI (GenAI) presents a promising approach, yet its application in this domain has received little attention to date. In this paper, we propose a GenAI-based framework to synthesize high-fidelity data directly from incomplete LEO network observations. We propose the representative data missing scenarios, and evaluate the performance with the latest GAN- and VAE-based GenAI models on the recent WetLinks dataset. We design block-wise and point-wise missing scenarios to closely simulate the data loss that happens on real-world LEO satellite networks. Our results show the effectiveness of our proposed GAN-based framework and GT-GAN model exhibits the best performance among all models in both missing scenarios. Even under extreme conditions (e.g., 40% of the input data is missing), GT-GAN shows the highest robustness, consistently capturing the underlying input data distribution and being the least affected in terms of generalization. Our results shed light on future directions for GenAI-based data augmentation methods and data-driven research on satellite network measurement.
